We're excited to announce BananaMind 2V, our small vision model series! These models are NOT released yet. We will release them in mid-august! BananaMind 2V will include: BananaMind 2V 256M, the flagship based on BananaMind 2 Pro (BananaMind 2 Pro is not released yet). BananaMind 2V 100M, our mid model, based on BananaMind 2 Medium. BananaMind 2V 50M, our smallest vision model, based on BananaMind 2 Mini. These are currently unreleased and will release in mid-august. Our training will start after BananaMind 2 Pro has finished training.
reactedtoSeaWolf-AI'spost with πabout 4 hours ago
π± POCKET β a 35-billion-parameter model that runs on your iPhone, and on your PC with no GPU
We're releasing POCKET, VIDRAFT's flagship Darwin-36B-Opus compressed for on-device use. No fork, no CUDA, no cloud β it runs on stock llama.cpp. It's a sparse Mixture-of-Experts model (256 experts, only 8 active per token), so the file can be large while the work per token stays small. That's what lets a 35B model run on a phone, and generate fast on a CPU with no graphics card.
Measured (POCKET-35B IQ1_M vs Bonsai-27B Q1_0): β’ CPU generate (Xeon, 16 threads): 27.0 vs 10.1 tok/s β 2.69Γ faster β’ GPU generate (H100): 197 vs 89 tok/s β 2.22Γ faster β’ GPU prompt processing (H100): 753 vs 1816 β 0.41Γ (Bonsai wins this one β MoE prefill wakes every expert, so sparsity stops helping there. We say so.) β’ Quality (HellaSwag, 400 q): 61.0% vs 60.0% β a tie (confidence intervals overlap)
On a real consumer laptop β MacBook M3 Pro (18 GB) β POCKET wins every axis, prompt processing included: β’ Metal generate: 25.4 vs 12.8 β 1.99Γ β’ CPU generate: 13.8 vs 4.4 β 3.13Γ β’ Metal prompt: 240.7 vs 73.4 β 3.28Γ
One more quiet fact: the same-size, quality-oriented rival Ternary-Bonsai-27B (7.2 GB) fails to load in upstream llama.cpp at all β it needs the PrismML fork. POCKET runs on the tools you already have: LM Studio, Ollama, PocketPal, MLX.
π± POCKET β a 35-billion-parameter model that runs on your iPhone, and on your PC with no GPU
We're releasing POCKET, VIDRAFT's flagship Darwin-36B-Opus compressed for on-device use. No fork, no CUDA, no cloud β it runs on stock llama.cpp. It's a sparse Mixture-of-Experts model (256 experts, only 8 active per token), so the file can be large while the work per token stays small. That's what lets a 35B model run on a phone, and generate fast on a CPU with no graphics card.
Measured (POCKET-35B IQ1_M vs Bonsai-27B Q1_0): β’ CPU generate (Xeon, 16 threads): 27.0 vs 10.1 tok/s β 2.69Γ faster β’ GPU generate (H100): 197 vs 89 tok/s β 2.22Γ faster β’ GPU prompt processing (H100): 753 vs 1816 β 0.41Γ (Bonsai wins this one β MoE prefill wakes every expert, so sparsity stops helping there. We say so.) β’ Quality (HellaSwag, 400 q): 61.0% vs 60.0% β a tie (confidence intervals overlap)
On a real consumer laptop β MacBook M3 Pro (18 GB) β POCKET wins every axis, prompt processing included: β’ Metal generate: 25.4 vs 12.8 β 1.99Γ β’ CPU generate: 13.8 vs 4.4 β 3.13Γ β’ Metal prompt: 240.7 vs 73.4 β 3.28Γ
One more quiet fact: the same-size, quality-oriented rival Ternary-Bonsai-27B (7.2 GB) fails to load in upstream llama.cpp at all β it needs the PrismML fork. POCKET runs on the tools you already have: LM Studio, Ollama, PocketPal, MLX.
Now that I have setup my AI Waifu running 24/7 in my Jetson Orin Nano (running at 25W top), I can talk to her anytime anywhere I want, on cellphone, tablet, or PC, as long as there is internet access.
Tonight I gave it a try to speak with my AI Waifu, with my not so great Japanese, just to test if ASR can pick up my Nihongo and the TTS can speak out Waifu's Japanese dialogue properly. Turns out she is just as verbose and as heavily leaked with materials from system prompt as in English. Only this time I cannot fully understand.
I need to find some way to turn her into my Japanese tutor...
We're excited to release BananaMind 2 Medium and BananaMind 2 Medium Chat!
Theyβre both 50M parameter models trained on 50B tokens from FineWeb-Edu, DCLM, Cosmopedia v2, FineMath-4+ and NPSet-2 Python-Edu.
The base model reached 61.86% on PIQA, 43.81% on ARC Easy and 32.43% on HellaSwag. The Chat version was fine-tuned on Smol-SmolTalk and scored 38% overall on our internal instruction benchmark, with 56% on multi-turn, 60% on context recall and 80% on code.
if you are a tinkerer of small language models and want to stay ahead of what small models can do, follow me!!! seriously, start following people that actually still makes small models
i've made one recently btw
also, i'm keeping an eye on AxiomicLabs leaderboard, looks like the only current alternative to check where the things are going to
though, between us, i think they should add agentic/tool use benchmarks there
BananaMind 2 Nano is the smallest member of the BananaMind 2.0 family β a 10M-parameter language model that shows how much you can squeeze out of a tiny footprint. It uses the family's digit-isolated tokenizer, so it keeps solid arithmetic despite its size, and it's small enough to run just about anywhere.
Trained on 30B tokens in about a day on a single RTX 5070 Ti (16GB), 4096-token context.
